Applying a self-organizing map to sensor-array characterization

R. A. Lemos, M. Nakamura, H. Kuwano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

As a basic application of neural networks, the authors implemented a self-organizing map (SOM) as an algorithm to classify the response vectors from a sensor array exposed to various chemical vapors. Our chemical sensing system consists of an array of piezoelectric quartz-crystal microbalance (QCM) sensors, each coated with a different polymer membrane. Typically, statistical analysis are employed to characterize the sensor response to various gases and to classify each individual gas. However, because the sorption-desorption cycle can require a long time to come to equilibrium, the initial vectors do not contain much unique information. We replaced principal-component analysis with the self-organizing map as a visual method of finding the time at which the sensor-array signals become unique and of estimating the quality of the extracted features. In addition, we found that the SOM can accurately classify response vectors faster than principal-component analysis.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Pages2009-2012
Number of pages4
ISBN (Print)0780314212
Publication statusPublished - 1993 Dec 1
Externally publishedYes
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3) - Nagoya, Jpn
Duration: 1993 Oct 251993 Oct 29

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2

Other

OtherProceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3)
CityNagoya, Jpn
Period93/10/2593/10/29

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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